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Slip-rate-dependent friction as a universal mechanism for slow slip events

Nature Geoscience volume 13, pages 705–710 (2020)Cite this article

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Abstract

A growing body of observations worldwide has documented fault slip transients that radiate little or no seismic energy. The mechanisms that govern these slow slip events (SSEs) and their wide range of depths, slip rates, durations, stress drops and recurrence intervals remain poorly known. Here we show that slow slip can be explained by a transition from rate-weakening frictional sliding at low slip rates towards rate-neutral or rate-strengthening behaviour at higher slip rates, as has been observed experimentally. We use numerical simulations to illustrate that this rate-dependent transition quantitatively explains the experimental data for natural fault rocks representative of materials in the source regions of SSEs. With a standard constant-parameter rate-and-state friction law, SSEs arise only near the threshold for slip instability. The inclusion of velocity-dependent friction parameters substantially broadens the range of conditions for slow slip occurrence, and produces a wide range of event characteristics, which include stress drop, duration and recurrence, as observed in nature. Upscaled numerical simulations that incorporate parameters consistent with laboratory measurements can reproduce geodetic observations of repeating SSEs on tectonic faults. We conclude that slip-rate-dependent friction explains the ubiquitous occurrence of SSEs in a broad range of geological environments.

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Fig. 1: Conditions for episodic slow slip.
Fig. 2: Vpeak and normalized stress drop as a function of κ.
Fig. 3: Characteristics of stick–slip events as a function of depth for a generic subduction megathrust.
Fig. 4: Comparison with observed SSEs.

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Data availability

GPS data for Hikurangi and Ryuku are publicly available at the Nevada Geodetic Laboratory (http://geodesy.unr.edu/NGLStationPages/stations/GISB.sta and http://geodesy.unr.edu/NGLStationPages/stations/J750.sta). Mexico GPS data35 and Cascadia inversion data33 are available at the Caltech data repository47 (https://doi.org/10.22002/D1.1286). Source data are provided with this paper.

Code availability

Simulation codes are available at Caltech data repository47 (https://doi.org/10.22002/D1.1286).

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Acknowledgements

We thank to J. Leeman, S. Michel and A. Gualandi for sharing data. This study was supported by NSF EAR-1821853 to J.-P.A., NSF EAR-1616664 and OCE-1334436 to D.S. and NSF EAR-1763305 and EAR-1520760 to C.M.

Author information

Authors and Affiliations

  1. Geology and Planetary Science Division, California Institute of Technology, Pasadena, CA, USA

    Kyungjae Im & Jean-Philippe Avouac

  2. Department of Energy and Mineral Engineering, EMS Energy Institute and G3 Center, The Pennsylvania State University, University Park, PA, USA

    Kyungjae Im

  3. Department of Geosciences, EMS Energy Institute, and G3 Center, The Pennsylvania State University, University Park, PA, USA

    Demian Saffer & Chris Marone

  4. University of Texas Institute for Geophysics and Dept. of Geological Sciences, University of Texas, Austin, TX, USA

    Demian Saffer

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Contributions

K.I. led the numerical modelling effort and writing of the manuscript. All the authors contributed to the interpretation of modelling results and writing the manuscript. D.S. and C.M. initiated the study and contributed to experimental data analysis. K.I. and J.-P.A. led the GPS data analysis.

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Correspondence to Kyungjae Im.

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Extended data

Extended Data Fig. 1 Evolution of peak velocity and stress drop with stability transition.

a, e, constant parameter, b, f, velocity dependent Dc, c, g, velocity dependent a, d, h, velocity dependent Dc and a cases. Panels a and d are identical to Figs. 1a and 1b, respectively. We used, Dc0 = 10 µm, SDc = 60 µm and VDc = 100 µm/s for velocity dependent Dc and a0 = 0.005, Sa = 0.0003, Va = 100 µm/s for velocity dependent a simulations.

Extended Data Fig. 2 Comparison of stability transition between laboratory data and simulation results.

Friction drop as a function of normal stress and loading velocity for a, laboratory experiments16, and b-e, simulations with b: constant parameters, c: velocity dependent Dc, d: velocity dependent a, and e: velocity dependence of both a and Dc. Simulation results in b-e are identical to Extended Data Fig. 1e–h, but re-sized to match the laboratory results of Panel a.

Extended Data Fig. 3 Experimental data for velocity dependence of friction parameters.

a, Experimental data showing Dc as a function of sliding velocity for quartz gouge29. Blue circles are measurements and solid line represents the velocity dependence we used in our laboratory scale simulations (Figs. 1 and 2). For tectonic fault zone simulations, we used identical SDc, but with Dc0 = 100 µm and VDc = 10−9 m/s. b, Compiled experimental data for a-b on tectonic fault zone materials27. Dashed line denotes the trend line of all measurement. We used the slop of the trendline (0.0013 per decade) for upscaled (Fig. 3) simulations.

Extended Data Fig. 4 Influence of simulation mass on earthquake slip rate.

Here we only considered constant parameter RSF cases, with high pore pressure. Red squares (M = 600000 kg/m2) are identical to main text Fig. 3 gray squares (Constant parameters high pore pressure). Blue and black squares show cases with one order of magnitude smaller and larger mass, respectively. Panel c shows that the peak velocity is dependent on the mass. However, even we assume significantly larger mass, stick slip abruptly evolves to fast rupture (Vpeak > 1 cm/s) at the transition.

Extended Data Fig. 5 Simple kinematic model for slip propagation.

a, Model illustration. We assume 200 km × 63 km slipping patch (light yellow) embedded in a half space with its lower edge at a depth of 26 km. For displacement of each patch, we impose the time evolution of slip derived from the spring-slider model adjusted to the Guerrero example (Fig. 4d). We considered three cases for slip propagation along the strike direction at: (i) 1 km/day, (ii) 5 km/day, and (iii) a case with simultaneous slip in the entire patch. Panel b, shows an example of slip propagation for the 1 km/day case. The fault slip is converted to surface deformation using an elastic dislocation (Okada) model48 and the normalized displacements are plotted in panels c&d, for comparison with the observed Guerrero gap GPS timeseries. The result shows that the case with a propagation rate of 5 km/day (red) is nearly indistinguishable from the case of simultaneous slip (equivalent to an infinitely fast propagation). The case with 1 km/day (blue) which is at the lower end of the typical rate of propagation of SSEs, is also only slightly altered by the effect of the propagation.

Source data

Source Data Fig. 1

Data points for Fig. 1.

Source Data Fig. 2

Data points for Fig. 2.

Source Data Fig. 3

Data points for Fig. 3.

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Im, K., Saffer, D., Marone, C. et al. Slip-rate-dependent friction as a universal mechanism for slow slip events. Nat. Geosci. 13, 705–710 (2020). https://doi.org/10.1038/s41561-020-0627-9

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